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   &#160;<span id="projectnumber">1.0</span>
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<a href="_c_m_c_l_a_8cs.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 ﻿using System;
<a name="l00002"></a>00002 <span class="keyword">using</span> System.Collections.Generic;
<a name="l00003"></a>00003 <span class="keyword">using</span> System.Linq;
<a name="l00004"></a>00004 <span class="keyword">using</span> System.Text;
<a name="l00005"></a>00005 
<a name="l00006"></a>00006 <span class="keyword">namespace </span>ClusterAggregation.ClusterAggregators
<a name="l00007"></a>00007 {
<a name="l00008"></a>00008     <span class="keyword">using</span> Datum;
<a name="l00009"></a>00009     <span class="keyword">using</span> DataSets;
<a name="l00010"></a>00010     <span class="keyword">using</span> Clusterers;
<a name="l00015"></a><a class="code" href="class_cluster_aggregation_1_1_cluster_aggregators_1_1_c_m_c_l_a.html">00015</a>     <span class="keyword">public</span> <span class="keyword">class </span><a class="code" href="class_cluster_aggregation_1_1_cluster_aggregators_1_1_c_m_c_l_a.html">CMCLA</a> : <a class="code" href="interface_cluster_aggregation_1_1_cluster_aggregators_1_1_i_consensus.html">IConsensus</a>
<a name="l00016"></a>00016     {
<a name="l00018"></a>00018         <span class="keyword">private</span> <span class="keywordtype">double</span>[,] m_distancesMatrix;
<a name="l00020"></a>00020         <span class="keyword">private</span> <a class="code" href="class_cluster_aggregation_1_1_datum_1_1_a_data.html">AData</a>[] m_metadata;
<a name="l00022"></a>00022         <span class="keyword">private</span> <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_cluster.html">CCluster</a>[] m_clsarray;
<a name="l00024"></a>00024         <span class="keyword">private</span> <a class="code" href="interface_cluster_aggregation_1_1_clusterers_1_1_i_clusterer.html">IClusterer</a> m_clusterer;
<a name="l00026"></a>00026         <span class="keyword">private</span> <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_cluster.html">CCluster</a>[] m_metaClusters;
<a name="l00028"></a>00028         <span class="keyword">private</span> uint[,] m_dataAfilliation;
<a name="l00030"></a>00030         <span class="keyword">private</span> <a class="code" href="class_cluster_aggregation_1_1_datum_1_1_a_data.html">AData</a>[] m_data;
<a name="l00032"></a>00032         <span class="keyword">private</span> <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a> m_resultPartition;
<a name="l00033"></a>00033 
<a name="l00038"></a><a class="code" href="class_cluster_aggregation_1_1_cluster_aggregators_1_1_c_m_c_l_a.html#a288331944f3f8ea002bd332b81bf335f">00038</a>         <span class="keyword">public</span> <a class="code" href="class_cluster_aggregation_1_1_cluster_aggregators_1_1_c_m_c_l_a.html">CMCLA</a>(<a class="code" href="interface_cluster_aggregation_1_1_clusterers_1_1_i_clusterer.html">IClusterer</a> clstrr = null)
<a name="l00039"></a>00039         {
<a name="l00040"></a>00040             m_clusterer = clstrr;
<a name="l00041"></a>00041             <span class="keywordflow">if</span> (clstrr == null)
<a name="l00042"></a>00042                 <span class="comment">//m_clusterer = new CPamImpl();</span>
<a name="l00043"></a>00043              m_clusterer = <span class="keyword">new</span> <a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_spectral_clusterer.html">CSpectralClusterer</a>(null, 0.3, <span class="keyword">true</span>);
<a name="l00044"></a>00044         }
<a name="l00045"></a>00045 
<a name="l00046"></a>00046 
<a name="l00047"></a><a class="code" href="class_cluster_aggregation_1_1_cluster_aggregators_1_1_c_m_c_l_a.html#aee553f43ba793804c4812783cbe3f915">00047</a>         <span class="keyword">public</span> <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a> ensemble(<a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a>[] partitions, <a class="code" href="class_cluster_aggregation_1_1_datum_1_1_a_data.html">AData</a>[] data, <span class="keywordtype">int</span> k)
<a name="l00048"></a>00048         {
<a name="l00049"></a>00049             lock (<span class="keyword">this</span>)
<a name="l00050"></a>00050             {
<a name="l00051"></a>00051                 <span class="comment">// initializations</span>
<a name="l00052"></a>00052                 m_data = data;
<a name="l00053"></a>00053                 m_resultPartition = <span class="keyword">new</span> <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a>();
<a name="l00054"></a>00054                 m_resultPartition.name = <span class="stringliteral">&quot;MCLA&quot;</span>;
<a name="l00055"></a>00055 
<a name="l00056"></a>00056                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; k; i++)
<a name="l00057"></a>00057                 {
<a name="l00058"></a>00058                     m_resultPartition.clusters.Add(<span class="keyword">new</span> <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_cluster.html">CCluster</a>());
<a name="l00059"></a>00059                 }
<a name="l00060"></a>00060                 <span class="comment">// end of initializations</span>
<a name="l00061"></a>00061 
<a name="l00062"></a>00062                 <span class="comment">// algorithm by steps - as defined by strehl and gosh</span>
<a name="l00063"></a>00063                 createHyperGraph(partitions);
<a name="l00064"></a>00064                 clusterHyperEdges(k);
<a name="l00065"></a>00065                 collapseHyperEdges();
<a name="l00066"></a>00066                 competeForObjects();
<a name="l00067"></a>00067 
<a name="l00068"></a>00068                 <span class="comment">//release data</span>
<a name="l00069"></a>00069                 <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a> rez = m_resultPartition;
<a name="l00070"></a>00070                 m_distancesMatrix = null;
<a name="l00071"></a>00071                 m_metadata = null;
<a name="l00072"></a>00072                 m_clsarray = null;
<a name="l00073"></a>00073                 m_metaClusters = null;
<a name="l00074"></a>00074                 m_dataAfilliation = null;
<a name="l00075"></a>00075                 m_data = null;
<a name="l00076"></a>00076                 m_resultPartition = null;
<a name="l00077"></a>00077                 rez.<a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html#ab47f8f65306a645f0ce17581643115bd">ANMI</a> = rez.<a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html#aa8487de094410cfdf6bfeec93019bc91">compareTo</a>(partitions);
<a name="l00078"></a>00078                 <span class="keywordflow">return</span> rez;
<a name="l00079"></a>00079             }
<a name="l00080"></a>00080         }
<a name="l00081"></a>00081 
<a name="l00082"></a>00082 
<a name="l00087"></a>00087         <span class="keyword">private</span> <span class="keywordtype">void</span> createHyperGraph(<a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a>[] partitions)
<a name="l00088"></a>00088         {
<a name="l00089"></a>00089 
<a name="l00090"></a>00090             <span class="comment">// join clusters to list</span>
<a name="l00091"></a>00091             LinkedList&lt;CCluster&gt; m_clusters = <span class="keyword">new</span> LinkedList&lt;CCluster&gt;();
<a name="l00092"></a>00092 
<a name="l00093"></a>00093             <span class="comment">// accumulate clusters</span>
<a name="l00094"></a>00094             <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; partitions.Length; i++)
<a name="l00095"></a>00095             {
<a name="l00096"></a>00096                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; partitions[i].<a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html#a7ea8662b7d1c6126b3e8b440ad40d0c7">_clusters</a>.Count; j++)
<a name="l00097"></a>00097                 {
<a name="l00098"></a>00098                     m_clusters.AddFirst(partitions[i]._clusters[j]);
<a name="l00099"></a>00099                 }
<a name="l00100"></a>00100             }
<a name="l00101"></a>00101             m_clsarray = m_clusters.ToArray();
<a name="l00102"></a>00102             m_distancesMatrix = <span class="keyword">new</span> <span class="keywordtype">double</span>[m_clsarray.Length, m_clsarray.Length];
<a name="l00103"></a>00103 
<a name="l00104"></a>00104             <span class="comment">// reset matrix</span>
<a name="l00105"></a>00105             <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; m_clsarray.Length; i++)
<a name="l00106"></a>00106             {
<a name="l00107"></a>00107                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; m_clsarray.Length; j++)
<a name="l00108"></a>00108                 {
<a name="l00109"></a>00109                     m_distancesMatrix[i, j] = 0;
<a name="l00110"></a>00110                 }
<a name="l00111"></a>00111             }
<a name="l00112"></a>00112 
<a name="l00113"></a>00113             <span class="comment">// creata data</span>
<a name="l00114"></a>00114             m_metadata = <span class="keyword">new</span> AData[m_clsarray.Length];
<a name="l00115"></a>00115             <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; m_metadata.Length; i++)
<a name="l00116"></a>00116             {
<a name="l00117"></a>00117                 m_metadata[i] = <span class="keyword">new</span> AData(0);
<a name="l00118"></a>00118                 m_metadata[i].id = i;
<a name="l00119"></a>00119             }
<a name="l00120"></a>00120 
<a name="l00121"></a>00121             <span class="comment">// calculate actual distance</span>
<a name="l00122"></a>00122             <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; m_clsarray.Length; i++)
<a name="l00123"></a>00123             {
<a name="l00124"></a>00124                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; m_clsarray.Length; j++)
<a name="l00125"></a>00125                 {
<a name="l00126"></a>00126                     m_distancesMatrix[i, j] = m_clsarray[i].compareTo(m_clsarray[j]);
<a name="l00127"></a>00127                 }
<a name="l00128"></a>00128             }
<a name="l00129"></a>00129 
<a name="l00130"></a>00130         } <span class="comment">// end of createHyperGraph</span>
<a name="l00131"></a>00131 
<a name="l00136"></a>00136         <span class="keyword">private</span> <span class="keywordtype">void</span> clusterHyperEdges( <span class="keywordtype">int</span> k)
<a name="l00137"></a>00137         {
<a name="l00138"></a>00138            <span class="comment">// m_distancesMatrix;</span>
<a name="l00139"></a>00139            <span class="comment">// m_metadata;</span>
<a name="l00140"></a>00140             ISimilarity tmpFunc = <span class="keyword">new</span> CMatrixBasedDistance(m_distancesMatrix);
<a name="l00141"></a>00141             CPartition tmpPartition = m_clusterer.cluster(m_metadata,tmpFunc,k);
<a name="l00142"></a>00142             m_metaClusters = tmpPartition._clusters.ToArray();
<a name="l00143"></a>00143         }
<a name="l00144"></a>00144 
<a name="l00148"></a>00148         <span class="keyword">private</span> <span class="keywordtype">void</span> collapseHyperEdges()
<a name="l00149"></a>00149         {
<a name="l00150"></a>00150             <span class="comment">// m_dataAfilliation actually indicates how much each datum belongs to each metaCluster</span>
<a name="l00151"></a>00151             m_dataAfilliation = <span class="keyword">new</span> uint[m_data.Length, m_metaClusters.Length];
<a name="l00152"></a>00152 
<a name="l00153"></a>00153             <span class="comment">// init matrix</span>
<a name="l00154"></a>00154             <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; m_data.Length; i++)
<a name="l00155"></a>00155             {
<a name="l00156"></a>00156                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; m_metaClusters.Length; j++)
<a name="l00157"></a>00157                 {
<a name="l00158"></a>00158                     m_dataAfilliation[i, j] = 0;
<a name="l00159"></a>00159                 }
<a name="l00160"></a>00160             }
<a name="l00161"></a>00161 
<a name="l00162"></a>00162             <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; m_data.Length; i++)  <span class="comment">// for each pixel</span>
<a name="l00163"></a>00163             {
<a name="l00164"></a>00164                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; m_metaClusters.Length; j++) <span class="comment">// for each meta cluster</span>
<a name="l00165"></a>00165                 {
<a name="l00166"></a>00166                     CCluster metaCls = m_metaClusters[j];
<a name="l00167"></a>00167 
<a name="l00168"></a>00168                     <span class="keywordflow">foreach</span> (AData cls <span class="keywordflow">in</span> metaCls.data)         <span class="comment">// for each cluster (metadata) in meta cluster</span>
<a name="l00169"></a>00169                     {
<a name="l00170"></a>00170                         <span class="keywordflow">if</span> (m_clsarray[cls.id].data.Contains(m_data[i]))
<a name="l00171"></a>00171                         {
<a name="l00172"></a>00172                             <span class="comment">// if cluster is contained</span>
<a name="l00173"></a>00173                             m_dataAfilliation[i, j]++; <span class="comment">// metaCls._data.Count</span>
<a name="l00174"></a>00174                         }
<a name="l00175"></a>00175                            
<a name="l00176"></a>00176                     }
<a name="l00177"></a>00177                 }
<a name="l00178"></a>00178             }
<a name="l00179"></a>00179 
<a name="l00180"></a>00180 
<a name="l00181"></a>00181 
<a name="l00182"></a>00182         }
<a name="l00183"></a>00183     
<a name="l00187"></a>00187         <span class="keyword">private</span> <span class="keywordtype">void</span> competeForObjects()
<a name="l00188"></a>00188         {
<a name="l00189"></a>00189             <span class="keywordtype">int</span> bestcluster = 0;
<a name="l00190"></a>00190             uint bestSum ;
<a name="l00191"></a>00191             <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; m_data.Length; i++)  <span class="comment">// for each pixel</span>
<a name="l00192"></a>00192             {
<a name="l00193"></a>00193                 bestSum = 0;
<a name="l00194"></a>00194                 bestcluster = 0;
<a name="l00195"></a>00195                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; m_metaClusters.Length; j++) <span class="comment">// for each meta cluster</span>
<a name="l00196"></a>00196                 {
<a name="l00197"></a>00197                     <span class="keywordflow">if</span> (m_dataAfilliation[i, j] &gt; bestSum)
<a name="l00198"></a>00198                     {
<a name="l00199"></a>00199                         bestcluster = j;
<a name="l00200"></a>00200                         bestSum = m_dataAfilliation[i, j];
<a name="l00201"></a>00201                     }
<a name="l00202"></a>00202                 } <span class="comment">// end of find max value and it&#39;s index</span>
<a name="l00203"></a>00203 
<a name="l00204"></a>00204                 <span class="comment">// add data i to best fitting cluster.</span>
<a name="l00205"></a>00205                 m_resultPartition.clusters[bestcluster].data.Add(m_data[i]);
<a name="l00206"></a>00206 
<a name="l00207"></a>00207             } <span class="comment">// end of loop - per pixel</span>
<a name="l00208"></a>00208 
<a name="l00209"></a>00209         }
<a name="l00210"></a>00210     }
<a name="l00211"></a>00211 }
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